Advances in the development of models and methods that can satisfactorily describe and analyze high dimensional systems are extremely valuable for many different disciplines including biology, meteorology, economics,social sciences, and engineering. These systems are characterized by nonlinearities and are difficult to understand.Computational methods governed by simple local rules have the potential of providing insightful interpretations and of paving the way towards quantitative and qualitative descriptions and understanding of complex systems.This project is focused on development of such methods and in particular on the development of a synergistic research and educational program in sequential Monte Carlo-based signal processing for high dimensional systems.

This research aims at laying the foundations of a sequential Monte Carlo methodology for high dimensional systems. In the literature, there are claims stating that particle filters cannot be used for complex systems because their random measures degenerate to single particles. While this is true for standard implementation of these filters, it does not hold true for alternative approaches. A new methodology based on the principle of divide and conquer is developed. In particular, the collapse of traditional particle filltering is avoided by setting an interconnected network of filters, each of them working on lower dimensional spaces. Research tasks include development of the theoretical grounds of the methods, establishment of guidelines for its use by practitioners,analysis of its accuracy, stability and scalability, and validation on a wide range of complex systems. The proposed methods are original and provide solutions to arguably the biggest open problem of particle filtering.

Agency
National Science Foundation (NSF)
Institute
Division of Computer and Communication Foundations (CCF)
Application #
0953316
Program Officer
John Cozzens
Project Start
Project End
Budget Start
2010-08-01
Budget End
2015-07-31
Support Year
Fiscal Year
2009
Total Cost
$314,019
Indirect Cost
Name
State University New York Stony Brook
Department
Type
DUNS #
City
Stony Brook
State
NY
Country
United States
Zip Code
11794